Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations344
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory37.8 KiB
Average record size in memory112.4 B

Variable types

Categorical5
Numeric7
Text1
Boolean1

Alerts

Body Mass (g) is highly overall correlated with Culmen Length (mm) and 3 other fieldsHigh correlation
Culmen Depth (mm) is highly overall correlated with Delta 15 N (o/oo) and 2 other fieldsHigh correlation
Culmen Length (mm) is highly overall correlated with Body Mass (g) and 2 other fieldsHigh correlation
Date Egg is highly overall correlated with Island and 3 other fieldsHigh correlation
Delta 13 C (o/oo) is highly overall correlated with Delta 15 N (o/oo) and 3 other fieldsHigh correlation
Delta 15 N (o/oo) is highly overall correlated with Body Mass (g) and 3 other fieldsHigh correlation
Flipper Length (mm) is highly overall correlated with Body Mass (g) and 3 other fieldsHigh correlation
Island is highly overall correlated with Date Egg and 1 other fieldsHigh correlation
Sample Number is highly overall correlated with Date Egg and 2 other fieldsHigh correlation
Species is highly overall correlated with Body Mass (g) and 7 other fieldsHigh correlation
studyName is highly overall correlated with Date Egg and 2 other fieldsHigh correlation
Clutch Completion is highly imbalanced (51.6%) Imbalance

Reproduction

Analysis started2025-02-14 12:15:25.241468
Analysis finished2025-02-14 12:15:34.421664
Duration9.18 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

studyName
Categorical

High correlation 

Distinct3
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
PAL0910
120 
PAL0809
114 
PAL0708
110 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters2408
Distinct characters8
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPAL0708
2nd rowPAL0708
3rd rowPAL0708
4th rowPAL0708
5th rowPAL0708

Common Values

ValueCountFrequency (%)
PAL0910 120
34.9%
PAL0809 114
33.1%
PAL0708 110
32.0%

Length

2025-02-14T17:45:34.560683image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-14T17:45:34.731900image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
pal0910 120
34.9%
pal0809 114
33.1%
pal0708 110
32.0%

Most occurring characters

ValueCountFrequency (%)
0 688
28.6%
P 344
14.3%
A 344
14.3%
L 344
14.3%
9 234
 
9.7%
8 224
 
9.3%
1 120
 
5.0%
7 110
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1376
57.1%
Uppercase Letter 1032
42.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 688
50.0%
9 234
 
17.0%
8 224
 
16.3%
1 120
 
8.7%
7 110
 
8.0%
Uppercase Letter
ValueCountFrequency (%)
P 344
33.3%
A 344
33.3%
L 344
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1376
57.1%
Latin 1032
42.9%

Most frequent character per script

Common
ValueCountFrequency (%)
0 688
50.0%
9 234
 
17.0%
8 224
 
16.3%
1 120
 
8.7%
7 110
 
8.0%
Latin
ValueCountFrequency (%)
P 344
33.3%
A 344
33.3%
L 344
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2408
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 688
28.6%
P 344
14.3%
A 344
14.3%
L 344
14.3%
9 234
 
9.7%
8 224
 
9.3%
1 120
 
5.0%
7 110
 
4.6%

Sample Number
Real number (ℝ)

High correlation 

Distinct152
Distinct (%)44.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.151163
Minimum1
Maximum152
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-02-14T17:45:34.928385image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.15
Q129
median58
Q395.25
95-th percentile134.85
Maximum152
Range151
Interquartile range (IQR)66.25

Descriptive statistics

Standard deviation40.430199
Coefficient of variation (CV)0.64021306
Kurtosis-0.9260372
Mean63.151163
Median Absolute Deviation (MAD)32
Skewness0.35140216
Sum21724
Variance1634.601
MonotonicityNot monotonic
2025-02-14T17:45:35.171728image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3
 
0.9%
2 3
 
0.9%
3 3
 
0.9%
4 3
 
0.9%
5 3
 
0.9%
6 3
 
0.9%
7 3
 
0.9%
8 3
 
0.9%
9 3
 
0.9%
10 3
 
0.9%
Other values (142) 314
91.3%
ValueCountFrequency (%)
1 3
0.9%
2 3
0.9%
3 3
0.9%
4 3
0.9%
5 3
0.9%
6 3
0.9%
7 3
0.9%
8 3
0.9%
9 3
0.9%
10 3
0.9%
ValueCountFrequency (%)
152 1
0.3%
151 1
0.3%
150 1
0.3%
149 1
0.3%
148 1
0.3%
147 1
0.3%
146 1
0.3%
145 1
0.3%
144 1
0.3%
143 1
0.3%

Species
Categorical

High correlation 

Distinct3
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
Adelie Penguin (Pygoscelis adeliae)
152 
Gentoo penguin (Pygoscelis papua)
124 
Chinstrap penguin (Pygoscelis antarctica)
68 

Length

Max length41
Median length35
Mean length35.465116
Min length33

Characters and Unicode

Total characters12200
Distinct characters23
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAdelie Penguin (Pygoscelis adeliae)
2nd rowAdelie Penguin (Pygoscelis adeliae)
3rd rowAdelie Penguin (Pygoscelis adeliae)
4th rowAdelie Penguin (Pygoscelis adeliae)
5th rowAdelie Penguin (Pygoscelis adeliae)

Common Values

ValueCountFrequency (%)
Adelie Penguin (Pygoscelis adeliae) 152
44.2%
Gentoo penguin (Pygoscelis papua) 124
36.0%
Chinstrap penguin (Pygoscelis antarctica) 68
19.8%

Length

2025-02-14T17:45:35.408973image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-14T17:45:35.669151image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
penguin 344
25.0%
pygoscelis 344
25.0%
adelie 152
11.0%
adeliae 152
11.0%
gentoo 124
 
9.0%
papua 124
 
9.0%
chinstrap 68
 
4.9%
antarctica 68
 
4.9%

Most occurring characters

ValueCountFrequency (%)
e 1420
 
11.6%
i 1128
 
9.2%
1032
 
8.5%
n 948
 
7.8%
a 824
 
6.8%
s 756
 
6.2%
g 688
 
5.6%
l 648
 
5.3%
o 592
 
4.9%
p 508
 
4.2%
Other values (13) 3656
30.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9640
79.0%
Space Separator 1032
 
8.5%
Uppercase Letter 840
 
6.9%
Open Punctuation 344
 
2.8%
Close Punctuation 344
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1420
14.7%
i 1128
11.7%
n 948
9.8%
a 824
8.5%
s 756
7.8%
g 688
7.1%
l 648
 
6.7%
o 592
 
6.1%
p 508
 
5.3%
c 480
 
5.0%
Other values (6) 1648
17.1%
Uppercase Letter
ValueCountFrequency (%)
P 496
59.0%
A 152
 
18.1%
G 124
 
14.8%
C 68
 
8.1%
Space Separator
ValueCountFrequency (%)
1032
100.0%
Open Punctuation
ValueCountFrequency (%)
( 344
100.0%
Close Punctuation
ValueCountFrequency (%)
) 344
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10480
85.9%
Common 1720
 
14.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1420
13.5%
i 1128
10.8%
n 948
 
9.0%
a 824
 
7.9%
s 756
 
7.2%
g 688
 
6.6%
l 648
 
6.2%
o 592
 
5.6%
p 508
 
4.8%
P 496
 
4.7%
Other values (10) 2472
23.6%
Common
ValueCountFrequency (%)
1032
60.0%
( 344
 
20.0%
) 344
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1420
 
11.6%
i 1128
 
9.2%
1032
 
8.5%
n 948
 
7.8%
a 824
 
6.8%
s 756
 
6.2%
g 688
 
5.6%
l 648
 
5.3%
o 592
 
4.9%
p 508
 
4.2%
Other values (13) 3656
30.0%

Island
Categorical

High correlation 

Distinct3
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
Biscoe
168 
Dream
124 
Torgersen
52 

Length

Max length9
Median length6
Mean length6.0930233
Min length5

Characters and Unicode

Total characters2096
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTorgersen
2nd rowTorgersen
3rd rowTorgersen
4th rowTorgersen
5th rowTorgersen

Common Values

ValueCountFrequency (%)
Biscoe 168
48.8%
Dream 124
36.0%
Torgersen 52
 
15.1%

Length

2025-02-14T17:45:35.887441image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-14T17:45:36.075290image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
biscoe 168
48.8%
dream 124
36.0%
torgersen 52
 
15.1%

Most occurring characters

ValueCountFrequency (%)
e 396
18.9%
r 228
10.9%
s 220
10.5%
o 220
10.5%
B 168
8.0%
c 168
8.0%
i 168
8.0%
D 124
 
5.9%
a 124
 
5.9%
m 124
 
5.9%
Other values (3) 156
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1752
83.6%
Uppercase Letter 344
 
16.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 396
22.6%
r 228
13.0%
s 220
12.6%
o 220
12.6%
c 168
9.6%
i 168
9.6%
a 124
 
7.1%
m 124
 
7.1%
g 52
 
3.0%
n 52
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
B 168
48.8%
D 124
36.0%
T 52
 
15.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 2096
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 396
18.9%
r 228
10.9%
s 220
10.5%
o 220
10.5%
B 168
8.0%
c 168
8.0%
i 168
8.0%
D 124
 
5.9%
a 124
 
5.9%
m 124
 
5.9%
Other values (3) 156
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2096
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 396
18.9%
r 228
10.9%
s 220
10.5%
o 220
10.5%
B 168
8.0%
c 168
8.0%
i 168
8.0%
D 124
 
5.9%
a 124
 
5.9%
m 124
 
5.9%
Other values (3) 156
 
7.4%
Distinct190
Distinct (%)55.2%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2025-02-14T17:45:36.492602image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.9011628
Min length4

Characters and Unicode

Total characters1686
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique76 ?
Unique (%)22.1%

Sample

1st rowN1A1
2nd rowN1A2
3rd rowN2A1
4th rowN2A2
5th rowN3A1
ValueCountFrequency (%)
n6a1 3
 
0.9%
n6a2 3
 
0.9%
n13a2 3
 
0.9%
n13a1 3
 
0.9%
n8a2 3
 
0.9%
n8a1 3
 
0.9%
n18a1 3
 
0.9%
n18a2 3
 
0.9%
n35a1 3
 
0.9%
n35a2 3
 
0.9%
Other values (180) 314
91.3%
2025-02-14T17:45:37.158117image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 344
20.4%
A 344
20.4%
2 260
15.4%
1 250
14.8%
3 82
 
4.9%
6 76
 
4.5%
4 70
 
4.2%
8 60
 
3.6%
5 60
 
3.6%
7 58
 
3.4%
Other values (2) 82
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 998
59.2%
Uppercase Letter 688
40.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 260
26.1%
1 250
25.1%
3 82
 
8.2%
6 76
 
7.6%
4 70
 
7.0%
8 60
 
6.0%
5 60
 
6.0%
7 58
 
5.8%
9 50
 
5.0%
0 32
 
3.2%
Uppercase Letter
ValueCountFrequency (%)
N 344
50.0%
A 344
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 998
59.2%
Latin 688
40.8%

Most frequent character per script

Common
ValueCountFrequency (%)
2 260
26.1%
1 250
25.1%
3 82
 
8.2%
6 76
 
7.6%
4 70
 
7.0%
8 60
 
6.0%
5 60
 
6.0%
7 58
 
5.8%
9 50
 
5.0%
0 32
 
3.2%
Latin
ValueCountFrequency (%)
N 344
50.0%
A 344
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1686
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 344
20.4%
A 344
20.4%
2 260
15.4%
1 250
14.8%
3 82
 
4.9%
6 76
 
4.5%
4 70
 
4.2%
8 60
 
3.6%
5 60
 
3.6%
7 58
 
3.4%
Other values (2) 82
 
4.9%

Clutch Completion
Boolean

Imbalance 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size476.0 B
True
308 
False
36 
ValueCountFrequency (%)
True 308
89.5%
False 36
 
10.5%
2025-02-14T17:45:37.343487image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Date Egg
Categorical

High correlation 

Distinct50
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
11/27/07
 
18
11/16/07
 
16
11/9/08
 
16
11/18/09
 
14
11/13/08
 
12
Other values (45)
268 

Length

Max length8
Median length8
Mean length7.7325581
Min length7

Characters and Unicode

Total characters2660
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11/11/07
2nd row11/11/07
3rd row11/16/07
4th row11/16/07
5th row11/16/07

Common Values

ValueCountFrequency (%)
11/27/07 18
 
5.2%
11/16/07 16
 
4.7%
11/9/08 16
 
4.7%
11/18/09 14
 
4.1%
11/13/08 12
 
3.5%
11/6/08 12
 
3.5%
11/21/09 12
 
3.5%
11/4/08 12
 
3.5%
11/14/08 10
 
2.9%
11/22/09 10
 
2.9%
Other values (40) 212
61.6%

Length

2025-02-14T17:45:37.518254image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
11/27/07 18
 
5.2%
11/16/07 16
 
4.7%
11/9/08 16
 
4.7%
11/18/09 14
 
4.1%
11/13/08 12
 
3.5%
11/6/08 12
 
3.5%
11/21/09 12
 
3.5%
11/4/08 12
 
3.5%
11/14/08 10
 
2.9%
11/22/09 10
 
2.9%
Other values (40) 212
61.6%

Most occurring characters

ValueCountFrequency (%)
1 844
31.7%
/ 688
25.9%
0 364
13.7%
9 164
 
6.2%
7 154
 
5.8%
2 150
 
5.6%
8 146
 
5.5%
3 46
 
1.7%
6 42
 
1.6%
4 32
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1972
74.1%
Other Punctuation 688
 
25.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 844
42.8%
0 364
18.5%
9 164
 
8.3%
7 154
 
7.8%
2 150
 
7.6%
8 146
 
7.4%
3 46
 
2.3%
6 42
 
2.1%
4 32
 
1.6%
5 30
 
1.5%
Other Punctuation
ValueCountFrequency (%)
/ 688
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2660
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 844
31.7%
/ 688
25.9%
0 364
13.7%
9 164
 
6.2%
7 154
 
5.8%
2 150
 
5.6%
8 146
 
5.5%
3 46
 
1.7%
6 42
 
1.6%
4 32
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2660
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 844
31.7%
/ 688
25.9%
0 364
13.7%
9 164
 
6.2%
7 154
 
5.8%
2 150
 
5.6%
8 146
 
5.5%
3 46
 
1.7%
6 42
 
1.6%
4 32
 
1.2%

Culmen Length (mm)
Real number (ℝ)

High correlation 

Distinct165
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.92193
Minimum32.1
Maximum59.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-02-14T17:45:37.714602image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum32.1
5-th percentile35.7
Q139.275
median44.25
Q348.5
95-th percentile51.985
Maximum59.6
Range27.5
Interquartile range (IQR)9.225

Descriptive statistics

Standard deviation5.4436433
Coefficient of variation (CV)0.12393907
Kurtosis-0.8634797
Mean43.92193
Median Absolute Deviation (MAD)4.65
Skewness0.053271789
Sum15109.144
Variance29.633252
MonotonicityNot monotonic
2025-02-14T17:45:37.957889image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.1 7
 
2.0%
45.2 6
 
1.7%
37.8 5
 
1.5%
39.6 5
 
1.5%
50 5
 
1.5%
46.5 5
 
1.5%
46.2 5
 
1.5%
45.5 5
 
1.5%
50.5 5
 
1.5%
39.7 4
 
1.2%
Other values (155) 292
84.9%
ValueCountFrequency (%)
32.1 1
0.3%
33.1 1
0.3%
33.5 1
0.3%
34 1
0.3%
34.1 1
0.3%
34.4 1
0.3%
34.5 1
0.3%
34.6 2
0.6%
35 2
0.6%
35.1 1
0.3%
ValueCountFrequency (%)
59.6 1
0.3%
58 1
0.3%
55.9 1
0.3%
55.8 1
0.3%
55.1 1
0.3%
54.3 1
0.3%
54.2 1
0.3%
53.5 1
0.3%
53.4 1
0.3%
52.8 1
0.3%

Culmen Depth (mm)
Real number (ℝ)

High correlation 

Distinct81
Distinct (%)23.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.15117
Minimum13.1
Maximum21.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-02-14T17:45:38.212255image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum13.1
5-th percentile13.9
Q115.6
median17.3
Q318.7
95-th percentile20
Maximum21.5
Range8.4
Interquartile range (IQR)3.1

Descriptive statistics

Standard deviation1.9690273
Coefficient of variation (CV)0.11480426
Kurtosis-0.89449651
Mean17.15117
Median Absolute Deviation (MAD)1.5
Skewness-0.14387981
Sum5900.0023
Variance3.8770686
MonotonicityNot monotonic
2025-02-14T17:45:38.425704image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 12
 
3.5%
17.9 10
 
2.9%
18.5 10
 
2.9%
18.6 10
 
2.9%
15 10
 
2.9%
19 9
 
2.6%
17.8 9
 
2.6%
17.3 9
 
2.6%
18.1 9
 
2.6%
18.9 9
 
2.6%
Other values (71) 247
71.8%
ValueCountFrequency (%)
13.1 1
 
0.3%
13.2 1
 
0.3%
13.3 1
 
0.3%
13.4 1
 
0.3%
13.5 2
 
0.6%
13.6 1
 
0.3%
13.7 6
1.7%
13.8 4
1.2%
13.9 4
1.2%
14 2
 
0.6%
ValueCountFrequency (%)
21.5 1
 
0.3%
21.2 2
0.6%
21.1 3
0.9%
20.8 1
 
0.3%
20.7 3
0.9%
20.6 1
 
0.3%
20.5 1
 
0.3%
20.3 3
0.9%
20.2 1
 
0.3%
20.1 1
 
0.3%

Flipper Length (mm)
Real number (ℝ)

High correlation 

Distinct56
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.9152
Minimum172
Maximum231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-02-14T17:45:38.637005image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum172
5-th percentile181
Q1190
median197
Q3213
95-th percentile225
Maximum231
Range59
Interquartile range (IQR)23

Descriptive statistics

Standard deviation14.020657
Coefficient of variation (CV)0.069783954
Kurtosis-0.97234932
Mean200.9152
Median Absolute Deviation (MAD)11
Skewness0.34668222
Sum69114.83
Variance196.57884
MonotonicityNot monotonic
2025-02-14T17:45:38.886352image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
190 22
 
6.4%
195 17
 
4.9%
187 16
 
4.7%
193 15
 
4.4%
210 14
 
4.1%
191 13
 
3.8%
215 12
 
3.5%
196 10
 
2.9%
197 10
 
2.9%
185 9
 
2.6%
Other values (46) 206
59.9%
ValueCountFrequency (%)
172 1
 
0.3%
174 1
 
0.3%
176 1
 
0.3%
178 4
1.2%
179 1
 
0.3%
180 5
1.5%
181 7
2.0%
182 3
0.9%
183 2
 
0.6%
184 7
2.0%
ValueCountFrequency (%)
231 1
 
0.3%
230 7
2.0%
229 2
 
0.6%
228 4
1.2%
226 1
 
0.3%
225 4
1.2%
224 3
0.9%
223 2
 
0.6%
222 6
1.7%
221 5
1.5%

Body Mass (g)
Real number (ℝ)

High correlation 

Distinct95
Distinct (%)27.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4201.7544
Minimum2700
Maximum6300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-02-14T17:45:39.116229image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum2700
5-th percentile3150
Q13550
median4050
Q34750
95-th percentile5650
Maximum6300
Range3600
Interquartile range (IQR)1200

Descriptive statistics

Standard deviation799.61306
Coefficient of variation (CV)0.19030457
Kurtosis-0.7057711
Mean4201.7544
Median Absolute Deviation (MAD)600
Skewness0.47169045
Sum1445403.5
Variance639381.04
MonotonicityNot monotonic
2025-02-14T17:45:39.355406image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3800 12
 
3.5%
3700 11
 
3.2%
3900 10
 
2.9%
3950 10
 
2.9%
3550 9
 
2.6%
4400 8
 
2.3%
3450 8
 
2.3%
3400 8
 
2.3%
4300 8
 
2.3%
3500 7
 
2.0%
Other values (85) 253
73.5%
ValueCountFrequency (%)
2700 1
 
0.3%
2850 2
0.6%
2900 4
1.2%
2925 1
 
0.3%
2975 1
 
0.3%
3000 2
0.6%
3050 4
1.2%
3075 1
 
0.3%
3100 1
 
0.3%
3150 4
1.2%
ValueCountFrequency (%)
6300 1
 
0.3%
6050 1
 
0.3%
6000 2
 
0.6%
5950 2
 
0.6%
5850 3
0.9%
5800 2
 
0.6%
5750 1
 
0.3%
5700 5
1.5%
5650 3
0.9%
5600 2
 
0.6%

Sex
Categorical

Distinct3
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
MALE
178 
FEMALE
165 
.
 
1

Length

Max length6
Median length4
Mean length4.9505814
Min length1

Characters and Unicode

Total characters1703
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st rowMALE
2nd rowFEMALE
3rd rowFEMALE
4th rowMALE
5th rowFEMALE

Common Values

ValueCountFrequency (%)
MALE 178
51.7%
FEMALE 165
48.0%
. 1
 
0.3%

Length

2025-02-14T17:45:39.581689image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-14T17:45:39.749954image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
male 178
51.7%
female 165
48.0%
1
 
0.3%

Most occurring characters

ValueCountFrequency (%)
E 508
29.8%
M 343
20.1%
A 343
20.1%
L 343
20.1%
F 165
 
9.7%
. 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1702
99.9%
Other Punctuation 1
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 508
29.8%
M 343
20.2%
A 343
20.2%
L 343
20.2%
F 165
 
9.7%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1702
99.9%
Common 1
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 508
29.8%
M 343
20.2%
A 343
20.2%
L 343
20.2%
F 165
 
9.7%
Common
ValueCountFrequency (%)
. 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1703
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 508
29.8%
M 343
20.1%
A 343
20.1%
L 343
20.1%
F 165
 
9.7%
. 1
 
0.1%

Delta 15 N (o/oo)
Real number (ℝ)

High correlation 

Distinct331
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.7333817
Minimum7.6322
Maximum10.02544
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-02-14T17:45:39.947060image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum7.6322
5-th percentile7.898478
Q18.307415
median8.687455
Q39.13617
95-th percentile9.685715
Maximum10.02544
Range2.39324
Interquartile range (IQR)0.828755

Descriptive statistics

Standard deviation0.54039241
Coefficient of variation (CV)0.061876651
Kurtosis-0.65171465
Mean8.7333817
Median Absolute Deviation (MAD)0.406475
Skewness0.24395225
Sum3004.2833
Variance0.29202395
MonotonicityNot monotonic
2025-02-14T17:45:40.171333image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.733381697 14
 
4.1%
8.36821 1
 
0.3%
8.76651 1
 
0.3%
8.66496 1
 
0.3%
9.18718 1
 
0.3%
9.4606 1
 
0.3%
9.13362 1
 
0.3%
8.63243 1
 
0.3%
8.55583 1
 
0.3%
9.18528 1
 
0.3%
Other values (321) 321
93.3%
ValueCountFrequency (%)
7.6322 1
0.3%
7.63452 1
0.3%
7.63884 1
0.3%
7.68528 1
0.3%
7.6887 1
0.3%
7.69778 1
0.3%
7.76843 1
0.3%
7.77672 1
0.3%
7.79958 1
0.3%
7.8208 1
0.3%
ValueCountFrequency (%)
10.02544 1
0.3%
10.02372 1
0.3%
10.02019 1
0.3%
9.98044 1
0.3%
9.93727 1
0.3%
9.88809 1
0.3%
9.8059 1
0.3%
9.80589 1
0.3%
9.79532 1
0.3%
9.77528 1
0.3%

Delta 13 C (o/oo)
Real number (ℝ)

High correlation 

Distinct332
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-25.686292
Minimum-27.01854
Maximum-23.78767
Zeros0
Zeros (%)0.0%
Negative344
Negative (%)100.0%
Memory size2.8 KiB
2025-02-14T17:45:40.389610image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum-27.01854
5-th percentile-26.789243
Q1-26.28546
median-25.79366
Q3-25.089467
95-th percentile-24.365844
Maximum-23.78767
Range3.23087
Interquartile range (IQR)1.1959925

Descriptive statistics

Standard deviation0.77876997
Coefficient of variation (CV)-0.030318505
Kurtosis-0.95217501
Mean-25.686292
Median Absolute Deviation (MAD)0.58581
Skewness0.34429116
Sum-8836.0843
Variance0.60648266
MonotonicityNot monotonic
2025-02-14T17:45:40.627506image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-25.68629154 13
 
3.8%
-25.33302 1
 
0.3%
-25.32426 1
 
0.3%
-25.29805 1
 
0.3%
-25.21799 1
 
0.3%
-24.89958 1
 
0.3%
-25.09368 1
 
0.3%
-25.21315 1
 
0.3%
-25.22588 1
 
0.3%
-25.06691 1
 
0.3%
Other values (322) 322
93.6%
ValueCountFrequency (%)
-27.01854 1
0.3%
-26.9547 1
0.3%
-26.89644 1
0.3%
-26.86485 1
0.3%
-26.86352 1
0.3%
-26.86127 1
0.3%
-26.84506 1
0.3%
-26.84415 1
0.3%
-26.84374 1
0.3%
-26.84272 1
0.3%
ValueCountFrequency (%)
-23.78767 1
0.3%
-23.89017 1
0.3%
-23.90309 1
0.3%
-24.10255 1
0.3%
-24.16566 1
0.3%
-24.17282 1
0.3%
-24.23592 1
0.3%
-24.25255 1
0.3%
-24.26375 1
0.3%
-24.29229 1
0.3%

Interactions

2025-02-14T17:45:32.704768image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-02-14T17:45:26.038315image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-02-14T17:45:27.141622image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-02-14T17:45:28.224833image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-02-14T17:45:29.216872image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-02-14T17:45:30.332715image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-02-14T17:45:31.677032image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-02-14T17:45:32.867056image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-02-14T17:45:26.201777image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-02-14T17:45:27.301732image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-02-14T17:45:28.378114image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-02-14T17:45:29.414345image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-02-14T17:45:30.494502image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-02-14T17:45:31.833140image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-02-14T17:45:33.018449image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-02-14T17:45:26.361468image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-02-14T17:45:27.443350image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-02-14T17:45:28.517806image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-02-14T17:45:29.579170image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-02-14T17:45:30.646260image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-02-14T17:45:31.977135image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-02-14T17:45:33.158536image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-02-14T17:45:26.506513image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-02-14T17:45:27.579240image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-02-14T17:45:28.639314image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-02-14T17:45:29.713419image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-02-14T17:45:30.780682image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-02-14T17:45:32.108612image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-02-14T17:45:33.361698image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-02-14T17:45:26.672536image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-02-14T17:45:27.736208image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-02-14T17:45:28.786624image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-02-14T17:45:29.869346image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-02-14T17:45:30.941765image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-02-14T17:45:32.259510image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-02-14T17:45:33.517752image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-02-14T17:45:26.833506image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-02-14T17:45:27.902898image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-02-14T17:45:28.935634image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-02-14T17:45:30.026225image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-02-14T17:45:31.091888image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-02-14T17:45:32.412690image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-02-14T17:45:33.664292image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-02-14T17:45:26.979419image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-02-14T17:45:28.067129image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-02-14T17:45:29.069523image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-02-14T17:45:30.171900image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-02-14T17:45:31.522172image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-02-14T17:45:32.553604image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Correlations

2025-02-14T17:45:40.796183image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Body Mass (g)Clutch CompletionCulmen Depth (mm)Culmen Length (mm)Date EggDelta 13 C (o/oo)Delta 15 N (o/oo)Flipper Length (mm)IslandSample NumberSexSpeciesstudyName
Body Mass (g)1.0000.055-0.4350.5830.019-0.376-0.5490.8410.4560.0090.4040.6030.000
Clutch Completion0.0551.0000.1960.0000.3480.0840.1490.0900.1230.1510.0000.1510.051
Culmen Depth (mm)-0.4350.1961.000-0.2240.1770.4200.608-0.5260.482-0.0490.3880.6320.106
Culmen Length (mm)0.5830.000-0.2241.0000.2380.144-0.1020.6730.320-0.2130.3390.6480.112
Date Egg0.0190.3480.1770.2381.0000.4460.3420.2630.6840.5620.0000.7610.929
Delta 13 C (o/oo)-0.3760.0840.4200.1440.4461.0000.542-0.3450.490-0.5070.0000.6830.637
Delta 15 N (o/oo)-0.5490.1490.608-0.1020.3420.5421.000-0.4920.4700.0070.0970.6090.303
Flipper Length (mm)0.8410.090-0.5260.6730.263-0.345-0.4921.0000.4980.0580.3050.6950.219
Island0.4560.1230.4820.3200.6840.4900.4700.4981.0000.4160.0000.6570.058
Sample Number0.0090.151-0.049-0.2130.562-0.5070.0070.0580.4161.0000.0000.2960.750
Sex0.4040.0000.3880.3390.0000.0000.0970.3050.0000.0001.0000.0000.000
Species0.6030.1510.6320.6480.7610.6830.6090.6950.6570.2960.0001.0000.000
studyName0.0000.0510.1060.1120.9290.6370.3030.2190.0580.7500.0000.0001.000

Missing values

2025-02-14T17:45:33.895128image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-14T17:45:34.268044image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

studyNameSample NumberSpeciesIslandIndividual IDClutch CompletionDate EggCulmen Length (mm)Culmen Depth (mm)Flipper Length (mm)Body Mass (g)SexDelta 15 N (o/oo)Delta 13 C (o/oo)
0PAL07081Adelie Penguin (Pygoscelis adeliae)TorgersenN1A1Yes11/11/0739.1000018.70000181.0000003750.000000MALE8.733382-25.686292
1PAL07082Adelie Penguin (Pygoscelis adeliae)TorgersenN1A2Yes11/11/0739.5000017.40000186.0000003800.000000FEMALE8.949560-24.694540
2PAL07083Adelie Penguin (Pygoscelis adeliae)TorgersenN2A1Yes11/16/0740.3000018.00000195.0000003250.000000FEMALE8.368210-25.333020
3PAL07084Adelie Penguin (Pygoscelis adeliae)TorgersenN2A2Yes11/16/0743.9219317.15117200.9152054201.754386MALE8.733382-25.686292
4PAL07085Adelie Penguin (Pygoscelis adeliae)TorgersenN3A1Yes11/16/0736.7000019.30000193.0000003450.000000FEMALE8.766510-25.324260
5PAL07086Adelie Penguin (Pygoscelis adeliae)TorgersenN3A2Yes11/16/0739.3000020.60000190.0000003650.000000MALE8.664960-25.298050
6PAL07087Adelie Penguin (Pygoscelis adeliae)TorgersenN4A1No11/15/0738.9000017.80000181.0000003625.000000FEMALE9.187180-25.217990
7PAL07088Adelie Penguin (Pygoscelis adeliae)TorgersenN4A2No11/15/0739.2000019.60000195.0000004675.000000MALE9.460600-24.899580
8PAL07089Adelie Penguin (Pygoscelis adeliae)TorgersenN5A1Yes11/9/0734.1000018.10000193.0000003475.000000MALE8.733382-25.686292
9PAL070810Adelie Penguin (Pygoscelis adeliae)TorgersenN5A2Yes11/9/0742.0000020.20000190.0000004250.000000MALE9.133620-25.093680
studyNameSample NumberSpeciesIslandIndividual IDClutch CompletionDate EggCulmen Length (mm)Culmen Depth (mm)Flipper Length (mm)Body Mass (g)SexDelta 15 N (o/oo)Delta 13 C (o/oo)
334PAL0910115Gentoo penguin (Pygoscelis papua)BiscoeN35A1Yes11/25/0946.2000014.10000217.0000004375.000000FEMALE8.302310-25.960130
335PAL0910116Gentoo penguin (Pygoscelis papua)BiscoeN35A2Yes11/25/0955.1000016.00000230.0000005850.000000MALE8.083540-26.181610
336PAL0910117Gentoo penguin (Pygoscelis papua)BiscoeN36A1Yes12/1/0944.5000015.70000217.0000004875.000000.8.041110-26.184440
337PAL0910118Gentoo penguin (Pygoscelis papua)BiscoeN36A2Yes12/1/0948.8000016.20000222.0000006000.000000MALE8.338250-25.885470
338PAL0910119Gentoo penguin (Pygoscelis papua)BiscoeN38A1No12/1/0947.2000013.70000214.0000004925.000000FEMALE7.991840-26.205380
339PAL0910120Gentoo penguin (Pygoscelis papua)BiscoeN38A2No12/1/0943.9219317.15117200.9152054201.754386MALE8.733382-25.686292
340PAL0910121Gentoo penguin (Pygoscelis papua)BiscoeN39A1Yes11/22/0946.8000014.30000215.0000004850.000000FEMALE8.411510-26.138320
341PAL0910122Gentoo penguin (Pygoscelis papua)BiscoeN39A2Yes11/22/0950.4000015.70000222.0000005750.000000MALE8.301660-26.041170
342PAL0910123Gentoo penguin (Pygoscelis papua)BiscoeN43A1Yes11/22/0945.2000014.80000212.0000005200.000000FEMALE8.242460-26.119690
343PAL0910124Gentoo penguin (Pygoscelis papua)BiscoeN43A2Yes11/22/0949.9000016.10000213.0000005400.000000MALE8.363900-26.155310